Bibliographic record
Abstract
Abstract Martin L. Friedland. The University of Toronto: A History. Toronto: University of Toronto Press, 2002. In 1969–1973, Princeton’s Shelby Cullom Davis Center for Historical Studies hosted a four-year Seminar on the history of education. A first outcome was three books edited by Lawrence Stone, but a longer-term result was broadened commitment among historians to social studies of the university. Mediævalists never doubted the value of university history, but their modernist cousins began to take it more seriously in and after the 1960s. Under the new dispensation, university historians would attend to the university’s social background. They would consider the political economies of public and private higher education. They would locate students and faculty in social classes-of-origin, in multiple cultural environments, not forgetting to explain power and administration inside the academy. The gossipy tales of collegiate intrigue that worked so well in Max Beerbohm’s Zuleika Dobson, F. M. Cornford’s Microcosmographia academica, C. P. Snow’s The Masters, and David Lodge’s Small World —and indeed in many a university history—would no longer do. If there was to be petite histoire (and narrativist, feminist, and post-modernist histories have considerable sympathy for it), it must discuss gender, ethnicity, urbanism, industry and class.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".